Generating diverse smart replies using synonym hierarchy

ABSTRACT

Techniques for generating diverse smart replies using a synonym hierarchy are disclosed herein. A computer system may detect that a first set of one or more messages having first content has been transmitted from a first computing device of a first user to a second computing device of a second user, determine a plurality of candidate replies based on the first content of the first set of one or more messages, and then select a plurality of smart replies from the plurality of candidate replies using a hierarchical graph data structure and at least one diversity rule. The selecting of the plurality of smart replies comprises omitting at least one of the plurality of candidate replies from selection based on the at least one diversity rule, which limits a number of the plurality of smart replies that have a common parent node.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of prior application Ser. No.16/020,148, filed on Jun. 27, 2018, which is incorporated by referenceherein in its entirety.

TECHNICAL FIELD

The present application relates generally to systems, methods, andcomputer program products for reducing the consumption of electronicresources in generating diverse smart replies for a user using synonymhierarchy graphs.

BACKGROUND

Generating suggested replies to messages can drain electronic resourcesby placing a heavy load of computational expense on the computer systemthat is generating the suggested replies. In attempting to provide userswith the most relevant and useful suggested replies, the computer systemmay have to evaluate a vast number of parameters for a model and mayrequire a large amount of training data for each and every user.Additionally, available space on a display screen can be limited,particularly with display screens of mobile devices. Computer systemsmay waste this importance space on the display screen by displaying anexcessive number of redundant smart replies. Other technical problemsmay arise as well.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments of the present disclosure are illustrated by way ofexample and not limitation in the figures of the accompanying drawings,in which like reference numbers indicate similar elements.

FIG. 1 is a block diagram illustrating a client-server system, inaccordance with an example embodiment.

FIG. 2 is a block diagram showing the functional components of a socialnetworking service within a networked system, in accordance with anexample embodiment.

FIG. 3 illustrates generated smart replies being displayed as selectableoptions for replying to messages within a graphical user interface (GUI)on a display screen of a mobile device, in accordance with an exampleembodiment.

FIG. 4 illustrates a result of one of the generated smart replies beingselected, in accordance with an example embodiment.

FIG. 5 illustrates another result of one of the generated smart repliesbeing selected, in accordance with an example embodiment.

FIG. 6 is a block diagram illustrating a communication system, inaccordance with an example embodiment.

FIG. 7 illustrates a hierarchical graph of synonym replies, inaccordance with an example embodiment.

FIG. 8 illustrates generated smart reply categories being displayed asselectable options for replying to messages within a GUI on a displayscreen of a mobile device, in accordance with an example embodiment.

FIG. 9 illustrates a result of one of the generated smart replycategories being selected, in accordance with an example embodiment.

FIG. 10 is a flowchart illustrating a method of generating smartreplies, in accordance with an example embodiment.

FIG. 11 is a flowchart illustrating a method of generating smartfollow-up contents, in accordance with an example embodiment.

FIG. 12 is a block diagram illustrating a mobile device, in accordancewith some example embodiments.

FIG. 13 is a block diagram of an example computer system on whichmethodologies described herein may be executed, in accordance with anexample embodiment.

DETAILED DESCRIPTION

Example methods and systems of generating diverse smart replies for auser using synonym hierarchy graphs are disclosed. In the followingdescription, for purposes of explanation, numerous specific details areset forth in order to provide a thorough understanding of exampleembodiments. It will be evident, however, to one skilled in the art thatthe present embodiments may be practiced without these specific details.

Some or all of the above problems may be addressed by one or moreexample embodiments disclosed herein. Some technical effects of thesystem and method of the present disclosure are to reducing theconsumption of electronic resources in generating diverse smart repliesfor a user. Additionally, other technical effects will be apparent fromthis disclosure as well.

In some example embodiments, operations are performed by a computersystem (or other machine) having a memory and at least one hardwareprocessor, with the operations comprising: detecting that a first set ofone or more messages having first content has been transmitted from afirst computing device of a first user to a second computing device of asecond user; determining a plurality of candidate replies based on thefirst content of the first set of one or more messages; selecting aplurality of smart replies from the plurality of candidate replies usinga hierarchical graph data structure and at least one diversity rule, thehierarchical graph data structure comprising a tree of concepts rangingfrom a root node to a plurality of leaf nodes with at least oneintermediate node in between the root node and each one of the pluralityof leaf nodes, each one of the plurality of smart replies beingrepresented by a corresponding one of the plurality of leaf nodes in thehierarchical graph data structure, the selecting the plurality of smartreplies comprising omitting at least one of the plurality of candidatereplies from selection to be included in the plurality of smart repliesbased on the at least one diversity rule, the at least one diversityrule limiting a number of the plurality of smart replies that have acommon parent node; and causing each one of the selected plurality ofsmart replies to be displayed on the second computing device of thesecond user as a corresponding selectable user interface element.

In some example embodiments, the operations further comprise: receivinga user selection of one of the plurality of smart replies from thesecond computing device; and transmitting a second message including theselected one of the plurality of smart replies to the first computingdevice in response to the receiving of the user selection.

In some example embodiments, the operations further comprise: selectinga plurality of smart follow-up content from a plurality of candidatefollow-up content in response to the transmitting of the second messageincluding the selected one of the plurality of smart replies to thefirst computing device, the selecting of the plurality of smartfollow-up content being based on at least one of the selected one of theplurality of smart replies and the first content of the first set of oneor more messages; and causing each one of the selected plurality ofsmart follow-up content to be displayed on the second computing deviceof the second user as a corresponding selectable user interface element.

In some example embodiments, the selecting of the plurality of smartfollow-up content comprises: determining the plurality of candidatefollow-up content based on at least one of the selected one of theplurality of smart replies and the first content of the first set of oneor more messages; and selecting the plurality of smart follow-up contentfrom the plurality of candidate follow-up content using the hierarchicalgraph data structure and the at least one diversity rule, each one ofthe plurality of smart follow-up content being represented by acorresponding one of the plurality of leaf nodes in the hierarchicalgraph data structure, the selecting of the plurality of smart follow-upcontent comprising omitting at least one of the plurality of candidatefollow-up content from selection to be included in the plurality ofsmart follow-up content based on the at least one diversity rule, the atleast one diversity rule limiting a number of the plurality of smartfollow-up content that have a common parent node.

In some example embodiments, the operations further comprise: receivinganother user selection of one of the plurality of smart follow-upcontent from the second computing device; and transmitting a thirdmessage including the selected one of the plurality of smart follow-upcontent to the first computing device in response to the receiving ofthe other user selection.

In some example embodiments, the first content of the first set of oneor more messages comprises text. In some example embodiments, each oneof the plurality of smart replies comprises text.

In some example embodiments, the computer system comprises a remoteserver. In some example embodiments, the computer system comprises thesecond computing device.

The methods or embodiments disclosed herein may be implemented as acomputer system having one or more modules (e.g., hardware modules orsoftware modules). Such modules may be executed by one or moreprocessors of the computer system. The methods or embodiments disclosedherein may be embodied as instructions stored on a machine-readablemedium that, when executed by one or more processors, cause the one ormore processors to perform the instructions.

FIG. 1 is a block diagram illustrating a client-server system 100, inaccordance with an example embodiment. A networked system 102 providesserver-side functionality via a network 104 (e.g., the Internet or WideArea Network (WAN)) to one or more clients. FIG. 1 illustrates, forexample, a web client 106 (e.g., a browser) and a programmatic client108 executing on respective client machines 110 and 112.

An Application Program Interface (API) server 114 and a web server 116are coupled to, and provide programmatic and web interfaces respectivelyto, one or more application servers 118. The application servers 118host one or more applications 120. The application servers 118 are, inturn, shown to be coupled to one or more database servers 124 thatfacilitate access to one or more databases 126. While the applications120 are shown in FIG. 1 to form part of the networked system 102, itwill be appreciated that, in alternative embodiments, the applications120 may form part of a service that is separate and distinct from thenetworked system 102.

Further, while the system 100 shown in FIG. 1 employs a client-serverarchitecture, the present disclosure is of course not limited to such anarchitecture, and could equally well find application in a distributed,or peer-to-peer, architecture system, for example. The variousapplications 120 could also be implemented as standalone softwareprograms, which do not necessarily have networking capabilities.

The web client 106 accesses the various applications 120 via the webinterface supported by the web server 116. Similarly, the programmaticclient 108 accesses the various services and functions provided by theapplications 120 via the programmatic interface provided by the APIserver 114.

FIG. 1 also illustrates a third party application 128, executing on athird party server machine 130, as having programmatic access to thenetworked system 102 via the programmatic interface provided by the APIserver 114. For example, the third party application 128 may, utilizinginformation retrieved from the networked system 102, support one or morefeatures or functions on a website hosted by the third party. The thirdparty website may, for example, provide one or more functions that aresupported by the relevant applications of the networked system 102.

In some embodiments, any website referred to herein may comprise onlinecontent that may be rendered on a variety of devices, including but notlimited to, a desktop personal computer, a laptop, and a mobile device(e.g., a tablet computer, smartphone, etc.). In this respect, any ofthese devices may be employed by a user to use the features of thepresent disclosure. In some embodiments, a user can use a mobile app ona mobile device (any of machines 110, 112, and 130 may be a mobiledevice) to access and browse online content, such as any of the onlinecontent disclosed herein. A mobile server (e.g., API server 114) maycommunicate with the mobile app and the application server(s) 118 inorder to make the features of the present disclosure available on themobile device.

In some embodiments, the networked system 102 may comprise functionalcomponents of a social networking service. FIG. 2 is a block diagramshowing the functional components of a social networking system 210,including a data processing module referred to herein as a communicationsystem 216, for use in social networking system 210, consistent withsome embodiments of the present disclosure. In some embodiments, thecommunication system 216 resides on application server(s) 118 in FIG. 1.However, it is contemplated that other configurations are also withinthe scope of the present disclosure.

As shown in FIG. 2, a front end may comprise a user interface module(e.g., a web server) 212, which receives requests from variousclient-computing devices, and communicates appropriate responses to therequesting client devices. For example, the user interface module(s) 212may receive requests in the form of Hypertext Transfer Protocol (HTTP)requests, or other web-based, application programming interface (API)requests. In addition, a member interaction detection module 213 may beprovided to detect various interactions that members have with differentapplications, services and content presented. As shown in FIG. 2, upondetecting a particular interaction, the member interaction detectionmodule 213 logs the interaction, including the type of interaction andany meta-data relating to the interaction, in a member activity andbehavior database 222.

An application logic layer may include one or more various applicationserver modules 214, which, in conjunction with the user interfacemodule(s) 212, generate various user interfaces (e.g., web pages) withdata retrieved from various data sources in the data layer. With someembodiments, individual application server modules 214 are used toimplement the functionality associated with various applications and/orservices provided by the social networking service. In some exampleembodiments, the application logic layer includes the communicationsystem 216.

As shown in FIG. 2, a data layer may include several databases, such asa database 218 for storing profile data, including both member profiledata and profile data for various organizations (e.g., companies,schools, etc.). Consistent with some embodiments, when a personinitially registers to become a member of the social networking service,the person will be prompted to provide some personal information, suchas his or her name, age (e.g., birthdate), gender, interests, contactinformation, home town, address, the names of the member's spouse and/orfamily members, educational background (e.g., schools, majors,matriculation and/or graduation dates, etc.), employment history,skills, professional organizations, and so on. This information isstored, for example, in the database 218. Similarly, when arepresentative of an organization initially registers the organizationwith the social networking service, the representative may be promptedto provide certain information about the organization. This informationmay be stored, for example, in the database 218, or another database(not shown). In some example embodiments, the profile data may beprocessed (e.g., in the background or offline) to generate variousderived profile data. For example, if a member has provided informationabout various job titles the member has held with the same company ordifferent companies, and for how long, this information can be used toinfer or derive a member profile attribute indicating the member'soverall seniority level, or seniority level within a particular company.In some example embodiments, importing or otherwise accessing data fromone or more externally hosted data sources may enhance profile data forboth members and organizations. For instance, with companies inparticular, financial data may be imported from one or more externaldata sources, and made part of a company's profile. Additionally, one ormore profile images (e.g., photos of the member) may be stored in thedatabase 218.

Once registered, a member may invite other members, or be invited byother members, to connect via the social networking service. A“connection” may require or indicate a bi-lateral agreement by themembers, such that both members acknowledge the establishment of theconnection. Similarly, with some embodiments, a member may elect to“follow” another member. In contrast to establishing a connection, theconcept of “following” another member typically is a unilateraloperation, and at least with some embodiments, does not requireacknowledgement or approval by the member that is being followed. Whenone member follows another, the member who is following may receivestatus updates (e.g., in an activity or content stream) or othermessages published by the member being followed, or relating to variousactivities undertaken by the member being followed. Similarly, when amember follows an organization, the member becomes eligible to receivemessages or status updates published on behalf of the organization. Forinstance, messages or status updates published on behalf of anorganization that a member is following will appear in the member'spersonalized data feed, commonly referred to as an activity stream orcontent stream. In any case, the various associations and relationshipsthat the members establish with other members, or with other entitiesand objects, are stored and maintained within a social graph, shown inFIG. 2 with database 220.

As members interact with the various applications, services, and contentmade available via the social networking system 210, the members'interactions and behavior (e.g., content viewed, links or buttonsselected, messages responded to, etc.) may be tracked and informationconcerning the member's activities and behavior may be logged or stored,for example, as indicated in FIG. 2 by the database 222. This loggedactivity information may then be used by the communication system 216.The members' interactions and behavior may also be tracked, stored, andused by a pre-fetch system 400 residing on a client device, such aswithin a browser of the client device, as will be discussed in furtherdetail below.

In some embodiments, databases 218, 220, and 222 may be incorporatedinto database(s) 126 in FIG. 1. However, other configurations are alsowithin the scope of the present disclosure.

Although not shown, in some embodiments, the social networking system210 provides an application programming interface (API) module via whichapplications and services can access various data and services providedor maintained by the social networking service. For example, using anAPI, an application may be able to request and/or receive one or morenavigation recommendations. Such applications may be browser-basedapplications, or may be operating system-specific. In particular, someapplications may reside and execute (at least partially) on one or moremobile devices (e.g., phone, or tablet computing devices) with a mobileoperating system. Furthermore, while in many cases the applications orservices that leverage the API may be applications and services that aredeveloped and maintained by the entity operating the social networkingservice, other than data privacy concerns, nothing prevents the API frombeing provided to the public or to certain third-parties under specialarrangements, thereby making the navigation recommendations available tothird party applications and services.

Although the communication system 216 is referred to herein as beingused in the context of a social networking service, it is contemplatedthat it may also be employed in the context of any website or onlineservices. Additionally, features of the present disclosure can be usedor presented in the context of a web page or any other user interfaceview, including, but not limited to, a user interface on a mobile deviceor on desktop software.

In some example embodiments, the communication system 216 is configuredto generate a diverse set of smart replies for a user using a synonymhierarchy graph and at least one diversity rule that limits the numberof smart replies that have a common parent node or that have some othercommon relationship with a particular node. A smart reply is a suggestedreply that is automatically generated by the communication system 216and presented to a user in response to, or otherwise based on, a messagesent to the user from another user (e.g., a text message transmittedfrom a first device of a first user to a second device of a seconduser).

FIG. 3 illustrates generated smart replies 330 being displayed asselectable options for replying to messages 310 within a graphical userinterface (GUI) on a display screen 305 of a mobile device 300, inaccordance with an example embodiment. In FIG. 3, a first user,represented on the display screen 305 by icon 312 (e.g., a profile imageof the first user), has sent a message 310 to a second user, representedin the display screen 305 by icon 332 (e.g., a profile image of thesecond user). Although the example conversations provided herein involvea first user and a second user, in some example embodiments, theconversations involve more than two users, such as with groupconversations where three or more users are included in the sameconversation thread. The GUI provides a content entry field 320configured to receive user-entered content (e.g., text, image) to beincluded in a reply message to the first user. The GUI also provides aselectable user interface element 325 configured to trigger thetransmission of the input received via the content entry field 320 tothe first user.

In FIG. 3, the communication system 216 has generated smart replies330A, 330B, and 330C based on the content of one or more of the messages310. In some example embodiments, the smart replies 330A, 330B, and 330Care each displayed as a selectable user interface element that thesecond user may select to include as part of a reply message to thefirst user. For example, each one of the smart replies 330A, 330B, and330C may comprise a selectable user interface element configured to, inresponse to a user selection of the smart reply 330 via thecorresponding selectable user interface element, transmit a replymessage including the selected smart reply 330 to the computing deviceof the first user. FIG. 4 illustrates a result of one of the generatedsmart replies 330 being selected by the second user, in accordance withan example embodiment. In FIG. 4, the second user has selected (e.g.,clicked on, tapped) the corresponding selectable user interface elementof smart reply 330B in FIG. 3. In response to this user selection of thecorresponding selectable user interface element of smart reply 330B, thecommunication system 216 has transmitted a reply message including theselected smart reply 330B to the computing device of the first user, asshown by the smart reply 330B being displayed as part of a conversationbetween the first user and the second user within the GUI on the displayscreen 305.

In some example embodiments, the communication system 216 generates anddisplays a plurality of smart follow-up content 340 in response to theselection of one of the smart replies 330 or in response to thetransmission of the second message including the selected smart reply330 to the first computing device. The plurality of smart follow-upcontent 340 may be determined based on at least one of the selectedsmart reply 330 and the content of the message 310 from the firstcomputing device.

In FIG. 4, the communication system 216 has generated smart follow-upcontents 340A, 340B, and 340C based on any combination of one or more ofthe selected smart reply 330B and the content of the message 310 fromthe first computing device. In some example embodiments, the smartfollow-up contents 340A, 340B, and 340C are each displayed as acorresponding selectable user interface element that the second user mayselect to include as part of a follow-up message to their precedingmessage that included the selected smart reply 330B. For example, eachone of the smart follow-up contents 340A, 340B, and 340C may comprise aselectable user interface element configured to, in response to a userselection of the smart follow-up content 340 via the correspondingselectable user interface element, transmit a follow-up messageincluding the selected smart follow-up content 340 to the computingdevice of the first user.

In some example embodiments, each selectable user interface elementcorresponding to one of the smart replies 330 is configured to, inresponse to a user selection of the smart reply 330 via thecorresponding selectable user interface element, insert the selectedsmart reply 330 into the content entry field 320, where the second usermay then provide an instruction to transmit a reply message includingthe selected smart reply 330 and any other user-entered content in thecontent entry field 320 to the first user. FIG. 5 illustrates anotherresult of one of the generated smart replies 330 being selected, inaccordance with an example embodiment. In FIG. 5, the second user hasselected (e.g., clicked on, tapped) the corresponding selectable userinterface element of smart reply 330B in FIG. 3. In response to thisuser selection of the corresponding selectable user interface element ofsmart reply 330B, the communication system 216 has inserted the selectedsmart reply 330B into the content entry field 320, but not yettransmitted a reply message including the selected smart reply 330B tothe first user. Here, the second user has an opportunity to enteradditional content (e.g., text, images) into the content entry field 320for inclusion along with the selected smart reply 330B in the replymessage or to edit (e.g., delete portions of) the selected smart reply330B in the content entry field 320 before transmitting the replymessage. When the second user is ready to send the content withincontent entry field 320 as a reply message to the first user, the seconduser selects the user interface element 325.

FIG. 6 is a block diagram illustrating the communication system 216, inaccordance with an example embodiment. In some example embodiments, thecommunication system 216 comprises any combination of one or more of aninterface module 610, a reply module 620, and one or more databases 630.The modules 610 and 620 and the database(s) 330 can reside on a computersystem, or other machine, having a memory and at least one processor(not shown).

In some example embodiments, the communication system 216 comprises aremote server. For example, in some embodiments, the modules 610 and 620and the database(s) 330 are incorporated into the application server(s)118 in FIG. 1, and the database(s) 330 is incorporated into database(s)126 in FIG. 1 and can include any combination of one or more ofdatabases 218, 220, and 222 in FIG. 2. In some example embodiments, thecommunication system 216 comprises a client computing device. Forexample, in some embodiments, any combination of one or more of themodules 610 and 620 and the database(s) 330 are incorporated into one ormore of the client machines 110 and 112 in FIG. 1 or the mobile device300 in FIG. 3. It is contemplated that other configurations of themodules 610 and 620, as well as the database(s) 630, are also within thescope of the present disclosure.

In some example embodiments, one or more of the modules 610 and 620 isconfigured to provide a variety of user interface functionality, such asgenerating user interfaces, interactively presenting user interfaces tothe user, receiving information from the user (e.g., interactions withuser interfaces), and so on. Presenting information to the user caninclude causing presentation of information to the user (e.g.,communicating information to a device with instructions to present theinformation to the user). Information may be presented using a varietyof means including visually displaying information and using otherdevice outputs (e.g., audio, tactile, and so forth). Similarly,information may be received via a variety of means includingalphanumeric input or other device input (e.g., one or more touchscreen, camera, tactile sensors, light sensors, infrared sensors,biometric sensors, microphone, gyroscope, accelerometer, other sensors,and so forth). In some example embodiments, one or more of the modules610 and 620 is configured to receive user input. For example, one ormore of the modules 610 and 620 can present one or more GUI elements(e.g., drop-down menu, selectable buttons, text field) with which a usercan submit input.

In some example embodiments, one or more of the modules 610 and 620 isconfigured to perform various communication functions to facilitate thefunctionality described herein, such as by communicating with the socialnetworking system 210 via the network 104 using a wired or wirelessconnection. Any combination of one or more of the modules 610 and 620may also provide various web services or functions, such as retrievinginformation from the third party servers 130 and the social networkingsystem 210. Information retrieved by the any of the modules 610 and 620may include profile data corresponding to users and members of thesocial networking service of the social networking system 210.

Additionally, any combination of one or more of the modules 610 and 620can provide various data functionality, such as exchanging informationwith the database(s) 630. For example, any of the modules 610 and 620can access profile data, social graph data, and member activity andbehavior data from the databases 218, 220, and 222 in FIG. 2, as well asexchange information with third party servers 130, client machines 110,112, and other sources of information.

In some example embodiments, the interface module 610 is configured todetect that a set of one or more messages having content has beentransmitted from a first computing device of a first user to a secondcomputing device of a second user. For example, in FIG. 3, the interfacemodule 610 has detected that message 310A or message 310B or bothmessages 310A and 310B have been transmitted to the first user.

In some example embodiments, the reply module 320 is configured togenerate one or more smart replies in response to, or otherwise basedon, the detection of the message(s) from the first user to the seconduser. In some example embodiments, the reply module 620 uses aclassification model to predict which replies best match the context inwhich a suggested reply is to be used (e.g., the conversation thus far,the interlocutors of the conversation, and other pertinent informationsuch as the time of day). However, different users employ differentdiction and phrasing to express the same idea. Although this variationamong users could be incorporated into a single core classificationmodel as, for example, a unique bias term for every individual user,this approach would add a vast number of parameters to the single coreclassification model (e.g., in a linear model, this would be one weightper user, per possible reply), would require a large amount of trainingdata for each user, and would be neither transparent nor adaptive toproduct-driven heuristics.

In some example embodiments, the reply module 620 uses a personalizationmodel separate from the core classification model. For each reply Rinitially selected by the core classification model with probabilityP_(c)(R), the reply module 620 finds a possible different personalizedreply, R′ that has the same or similar meaning. The reply module 620selects this final R′ by computing a probability or score for allpossible candidate replies, P(R′), combining the probability given bythe core classification model for the original reply, P_(c)(R), and thepersonalization model's conditional probability for the final reply R′given the original reply, P_(p)(R′|R). In some example embodiments, thereply module 620 employs the following combination of the coreclassification model (e.g., a first model) and the personalization model(e.g., a second model):

${{P\left( R^{\prime} \right)} = {\frac{1}{z}{P_{c}(R)}*{P_{p}\left( R^{\prime} \middle| R \right)}^{w}}},$

where w is a weighting exponent that determines the relative importanceof the personalization model (e.g., a higher weight corresponds topersonalization mattering relatively more) and Z is a normalization termto ensure that all the probabilities of all possible replies sum to 1.In some example embodiments, the personalization model is configured torely or to otherwise be based on two sources of information: a synonymhierarchy and observations of the replies selected by users.

In some example embodiments, the synonym hierarchy comprises a tree ofconcepts ranging from the most general root nodes (e.g., gratitude,yes/no answers, conversation openers, etc.) to the most specific leaves,which are the surface forms that are used as the actual smart replies.In some example embodiments, the synonym hierarchy comprises a directedacyclic graph and thus, e.g., a reply with multiple meanings can havemultiple parents.

FIG. 7 illustrates a hierarchical graph 700 of synonym replies, inaccordance with an example embodiment. The hierarchical graph 700comprises a root node 710, intermediate nodes 720, and leaf nodes 730.The root node 710 represents a general meaning of gratitude. Thisgeneral meaning of gratitude has different generalized forms ofexpression, such as “THANK YOU” and “APPRECIATED”, which are representedby intermediate nodes 720. These generalized forms of expression can bedivided into more specific levels of expression, which can then bedivided into even more specific levels of expression, and so on and soforth, until reaching the leaf nodes 730, which represent the surfaceforms that are to be considered for use as smart replies, such as“THANKS!”, “THANKS”, “THANK YOU!”, “THANK YOU”, “APPRECIATE IT”, and“APPRECIATE IT!” in FIG. 7. The leaf nodes 730 are synonyms of eachother.

In some example embodiments, the reply module 620 uses a history of thesecond user's interactions with smart replies to determine which leafnodes to select for presentation as smart replies to the second user. Insome example embodiments, every time smart replies 330 are presented tothe second user, the interface module 610 records the instance of suchpresentation, as well as which, if any, of the smart replies 330 wereselected by the second user for inclusion in a reply message to thefirst user.

In some example embodiments, the reply module 620 is configured to usethe synonym hierarchy in conjunction with the history of the user'sinteractions with smart replies to determine P_(p)(R′|R). For example,let the number of times user U has selected a reply that is a leaf belowa node Sin the hierarchy (where S itself may be a leaf, e.g., a surfaceform/reply, in which case its sole leaf is itself) be C_(U)(S), and letthe number of times the user saw a reply that is a leaf below a node Sbe C_(N)(S). Then, in some example embodiments, the reply module 620uses the following formulation of the personalization model:

${{P_{p}\left( R^{\prime} \middle| R \right)} = {\frac{1}{Z}D^{{LCA}{({R,R^{\prime}})}{\prod\limits_{i = 0}^{n - 1}\;{(\frac{{C_{U}{(R_{i}^{\prime})}} + L}{{C_{N}{(R_{i}^{\prime})}} + L})}^{x_{i}}}}}},$

where Z is a normalization factor to ensure that P_(p) is adistribution, D is a [0, 1] discount factor that rewards alternatereplies R′ that are “closer” to R, LCA( . . . ) is a function thatreturns the height (measured from the leaf) of the least common ancestorof R and R′, and LCA(R, R)=0.

One example of a synonym hierarchy comprises the following path fromroot to leaf for the reply “Thanks a lot.”: Gratitude (having alevel/height=general meaning)→Thank you (having alevel/height=metasynset)→Thanks a bunch (having alevel/height=synset)→Thanks a lot (having a level/height=near duplicateset)→“Thanks a lot.” (having a level/height=surface form/actual reply)

Using this example synonym hierarchy and the example formulation of thepersonalization model above, if R is “Thanks much” and R′ is “Thanks alot”, they do not share an ancestor at height 1 (the near duplicatelevel), but they do share the “Thanks a bunch” ancestor at height 2 (thesynset level). So LCA(“Thanks much”, “Thanks a lot”)=2. This is anexponent on D, and thus results in lower probabilities for personalizedreplies that have a higher common ancestor (and thus more disparatemeaning) relative to the original reply. In some example embodiments, nis the total height of the synonym hierarchy (i iterates over each levelof the tree), and R_(i)′ is the ith parent of the personalized reply R′in the synonym hierarchy. For example, R₀′=R, and R₂′ is the synset ofR′. L_(i) is an additive smoothing constant for each level of the treei, and X is a log-linear weight that determines the relative importanceof the ith layer in the synonym hierarchy. In some example embodiments,rather than finding a log-linear weighted average as in the model above,the reply module 620 instead uses a backoff model: using the user'sobserved CTR (click-through rate) for R′ if the impression and clickcounts are sufficiently high, and otherwise using the aggregated countsfor the parent of R′ in the synonym hierarchy (recursing to the nexthigher level if that aggregated impression count is still insufficient,and so on).

In some example embodiments, the reply module 620 uses the synonymhierarchy to smooth the possibly sparse counts for any given user,similar to ngram backoff and smoothing in neuro-linguistic programming(NLP), and to encourage exploration (e.g., trying smart replies with lowimpression counts—this is accomplished in the model described above bythe additive smoothing constants L_(i) that give a fixed number ofhallucinated clicks and impressions to each reply, which makeslow-impression-count replies unduly probable).

In some example embodiments, the reply module 620 is configured todetermine a plurality of candidate replies based on the first content ofthe first set of one or more messages, and then select a plurality ofsmart replies from the plurality of candidate replies using ahierarchical graph data structure and at least one diversity rule. Thehierarchical graph data structure comprises a tree of concepts rangingfrom a root node to a plurality of leaf nodes with at least oneintermediate node in between the root node and each one of the pluralityof leaf nodes, and each one of the plurality of candidate replies beingrepresented by a corresponding one of the plurality of leaf nodes in thehierarchical graph data structure, as discussed above with respect toFIG. 7. Since there is often a limited amount of screen space oncomputing devices for displaying content, particularly with mobiledevices, such as smartphones and smartwatches, a technical problemarises in how to most efficiently and effectively make use of thelimited screen space. The reply module 620 may use the diversity rule(s)to ensure that a minimum amount of diversity is present in the smartreplies 330 that are displayed within the GUI on the display screen 305of the mobile device 300.

In some example embodiments, each diversity rule is configured torestrict the number of displayed smart replies 330 that have a commonparent node or that have some other common relationship with aparticular node to being within a specified maximum threshold. Forexample, one diversity rule may require that no more than two displayedsmart replies 330 may have the same parent node. Referring to FIG. 7,this example diversity rule would prevent more than two leaf nodes 730having the same intermediate node 720 as their parent node from beingused as smart replies 330. For example, if an intermediate node 720 hadthree leaf nodes 730 as its children, the example diversity rule wouldcause the reply module 620 to select no more than two of the three childleaf nodes 720 of intermediate node 730 for use as smart replies 330. Inaddition to restrictions based on child-parent relationships, thediversity rules may also place restrictions on other types ofrelationships between nodes as well, such as a more distant ancestorsand descendants (e.g., grandparent-grandchild relationships). In someexample embodiments, the reply module 620 is configured to omit at leastone of the plurality of candidate replies from selection to be includedin the plurality of smart replies 330 based on the diversity rule(s).

In some example embodiments, the interface module 610 is configured tocause each one of the plurality of smart replies 330 to be displayed onthe second computing device of the second user as a correspondingselectable user interface element, as discussed above with respect toFIG. 3. In some example embodiments, the reply module 620 is configuredto receive a user selection of one of the plurality of smart replies 330from the second computing device, and then transmit a second messageincluding the selected one of the plurality of smart replies 330 to thefirst computing device in response to, or otherwise based on, thereceiving of the user selection, as discussed above with respect to FIG.4. In some example embodiments, the reply module 620 is configured toreceive a user selection of one of the plurality of smart replies fromthe second computing device, and then insert the selected one of theplurality of smart replies into a content entry field displayed on thesecond computing device in response to, or otherwise based on, thereceiving of the user selection, as discussed above with respect to FIG.5.

In some example embodiments, the communication system 216 is configuredto store a record of the user selection of the one of the plurality ofsmart replies in a database, such as in the database(s) 630, and thenmodify the second model based on the record of the user selection of theone of the plurality of smart replies using one or more machine learningoperations. The communication system 216 uses the record of the userselection of the one of the plurality of smart replies as training datain the one or more machine learning operations to modify the secondmodel.

In some instances, a cold-start problem may be encountered: until theuser sees some smart replies and clicks on them, there is no informationavailable about the user's preferences regarding smart replies. In orderto mitigate this problem, the reply module 620 may rely on one or moregroups to which the user belongs. In some example embodiments, the replymodule 620 accesses the profile information of the user and the profileinformation of other users to determine a group of other users who haveprofile information that has a level of similarity that is determined tosatisfy a predetermined similarity threshold. For example, the replymodule 620 may analyze the profile information of the user and the otherusers to determine groups of users who have the same or similarlocation, have the same or similar age, have the same gender, have thesame or similar culture, have the same or similar industry, have profileinformation embeddings that are within some distance K from the user'sprofile information embedding, etc.

A user may belong to multiple groups. For each group, the reply module620 may employ a model P_(p)(R′|R) as before, but use the user selectiondata aggregated across all users in the group. In some exampleembodiments, the model based on the observations for the user U is P_(p)^(u)(R′|R), the model for each group G is P_(p) ^(g)(R′|R), and the newmodel, smoothed via group membership, is:

${\frac{1}{Z}{P_{p}^{u}\left( R^{\prime} \middle| R \right)}{\prod_{g}{P_{p}^{g}\left( R^{\prime} \middle| R \right)}^{w_{g}}}},$

where w_(g) is a per-group weight that controls the relative influenceof each group, which may be machine-learned from data. In somealternative example embodiments, the reply module 620 smooths the userselection data over groups directly rather than smoothing theprobabilities from per-group models. For example, the reply module 620may use a weighted sum or backoff on the selection/impression counts(e.g., using a weighted average of the user's own counts and the groupsto which they belong).

The reply module 620 may determine whether or not to use theabove-discussed group-based model based on a determination of whether ornot there is sufficient user selection data for the second user. In someexample embodiments, the reply module 620 is configured to, ingenerating the plurality of smart replies, determine that a number oftimes the corresponding synonym reply was presented to the second useras a selectable option for replying to messages is below a predeterminedthreshold (e.g., the predetermined threshold can be one impressioninstance), and then, based on the determination that the number of timesthe corresponding synonym reply was presented to the second user isbelow the predetermined threshold, identify a group of users to whichthe second user belongs based on a determination that a level ofsimilarity between profile information of the second user and profileinformation of the group of users satisfies a predetermined similaritythreshold. The reply module 620 then generates the plurality of smartreplies using the second model based on the plurality of synonym repliesand a number of times the group of users has selected the correspondingsynonym reply for replying to messages.

Since different computing devices have different display screen sizes,displaying the same number of smart replies on every computing devicemay cause a technical problem. The screen size of smaller computingdevices, such as smartphones or smartwatches, may be insufficient tosupport the display of the same number of smart replies as on a largercomputing device, such as a desktop computer. In order to address thistechnical problem, in some example embodiments, the reply module 620 isconfigured to, in generating the plurality of smart replies, limit anumber of the smart replies in the plurality of smart replies to no morethan a particular number based on a screen size of the second computingdevice. For example, the reply module 620 may limit the number of smartreplies to be displayed on smartphones and smartwatches to no more thanthree smart replies, while limiting the number of smart replies to bedisplayed on laptop computers and tablet computers to no more than fivesmart replies. It is contemplated that other numbers and configurationsmay be used in limiting the number of smart replies to be displayed.

In some example embodiments, the reply module 620 is configured toselect a plurality of smart follow-up content from a plurality ofcandidate follow-up content in response to the transmitting of thesecond message including the selected one of the plurality of smartreplies to the first computing device. The selecting of the plurality ofsmart follow-up content is based on at least one of the selected one ofthe plurality of smart replies and the first content of the first set ofone or more messages. In some example embodiments, the reply module 620is configured to generate the plurality of smart follow-up content byusing the same model or a similar model as the model used to generatethe smart replies. However, the reply module 620 may feed the selectedsmart reply into the model in order to generate the plurality of smartfollow-up content. In some example embodiments, the reply module 620does not generate any smart follow-up content in response to input beingentered by the second user via the content entry field 320, but ratherconditions the generation and display of the smart follow-up content onthe second user selecting a smart reply or selecting a smart follow-up.In this respect, the reply module 620 may use only a selected smartreply or a selected smart follow-up content as the trigger and the basisfor generating and displaying a smart-follow-up content or an additionalsmart follow-up content.

In some example embodiments, the reply module 620 selects the pluralityof smart follow-up content by determining the plurality of candidatefollow-up content based on at least one of the selected one of theplurality of smart replies and the first content of the first set of oneor more messages, and then selecting the plurality of smart follow-upcontent from the plurality of candidate follow-up content using thehierarchical graph data structure and the diversity rule(s), with eachone of the plurality of smart follow-up content being represented by acorresponding one of the plurality of leaf nodes in the hierarchicalgraph data structure. In some example embodiments, the reply module 620is configured to omit at least one of the plurality of candidatefollow-up content from selection to be included in the plurality ofsmart follow-up content based on the diversity rule(s), where thediversity rule(s) limit a number of the plurality of smart follow-upcontent that have a common parent node or some other common relationshipwith a particular node.

In some example embodiments, the interface module 610 is configured tocause each one of the plurality of smart follow-up content to bedisplayed on the second computing device of the second user as acorresponding selectable user interface element, similar to the displayof the selectable user interface elements corresponding to the smartreplies. In some example embodiments, the interface module 610 is alsoconfigured to receive a user selection of one of the plurality of smartfollow-up content from the second computing device, and then transmit athird message including the selected one of the plurality of smartfollow-up content to the first computing device in response to receivingthe other user selection.

In some example embodiments, in order to conserve screen space whilestill providing the user with a maximum amount of options for smartreplies and smart follow-up content, the interface module 610 isconfigured to first display the corresponding category concepts of aplurality of intermediate nodes 720 as selectable user interfaceelements. FIG. 8 illustrates generated smart reply categories 830 beingdisplayed as or along with corresponding selectable options for replyingto messages within a GUI on the display screen 305 of the mobile device300, in accordance with an example embodiment. In FIG. 8, two categoryconcepts 830A and 830B of smart replies are displayed along withcorresponding selectable user interface elements 835A and 835B. Thecorresponding selectable user interface elements 835A and 835B are eachconfigured to, in response to being selected, cause the smart replies930 corresponding to the child nodes of the intermediate nodecorresponding to the selected user interface element 835 to be displayedas selectable smart replies. FIG. 9 illustrates a result of one of theselectable user interface elements 835 for the smart reply categories830 being selected, in accordance with an example embodiment. In theexample shown in FIG. 9, the selectable user interface element 835Acorresponding to the concept of “THANK YOU” has been selected in FIG. 8,thereby resulting in the expansion of the “THANK YOU” concept into threeselectable smart replies 930A, 930B, and 930C that have the samefunctionality as the selectable smart replies 330A, 330B, and 330Cdiscussed above with respect to FIG. 3.

FIG. 10 is a flowchart illustrating a method 1000 of generating smartreplies, in accordance with an example embodiment. The method 1000 canbe performed by processing logic that can comprise hardware (e.g.,circuitry, dedicated logic, programmable logic, microcode, etc.),software (e.g., instructions run on a processing device), or acombination thereof. In one implementation, the method 1000 is performedby the communication system 216 of FIGS. 2-3, as described above.

At operation 1010, the communication system 216 detects that a first setof one or more messages having first content has been transmitted from afirst computing device of a first user to a second computing device of asecond user. In some example embodiments, the first content of the firstset of one or more messages comprises text. However, it is contemplatedthat other types of content are also within the scope of the presentdisclosure, including, but not limited to, image-based content (e.g.,emojis, GIFs).

At operation 1020, the communication system 216 determines a pluralityof candidate replies based on the first content of the first set of oneor more messages. In some example embodiments, the communication system216 searches a database of candidate replies using the first content inorder to determine the plurality of candidate replies. For example, thecommunication system 216 may analyze the first content of the first setof one or more messages using a model to determine that it is a textmessage expressing “congratulations,” and then search the database ofcandidate replies for candidate replies that have been tagged as beingreplies to messages expressing “congratulations.” However, thecommunication system 216 may employ other techniques and operations indetermining the plurality of candidate replies.

At operation 1030, the communication system 216 selects a plurality ofsmart replies from the plurality of candidate replies using ahierarchical graph data structure and at least one diversity rule. Insome example embodiments, the hierarchical graph data structurecomprises a tree of concepts ranging from a root node to a plurality ofleaf nodes with at least one intermediate node in between the root nodeand each one of the plurality of leaf nodes, with each one of theplurality of smart replies being represented by a corresponding one ofthe plurality of leaf nodes in the hierarchical graph data structure. Insome example embodiments, the selecting the plurality of smart repliescomprises omitting at least one of the plurality of candidate repliesfrom selection to be included in the plurality of smart replies based onthe diversity rule(s), where the diversity rule(s) limit a number of theplurality of smart replies that have a common parent node or some othercommon relationship with a particular node. In some example embodiments,each one of the plurality of smart replies comprises text. However, itis contemplated that other types of smart replies are also within thescope of the present disclosure, including, but not limited to,image-based content (e.g., emojis, GIFs).

At operation 1040, the communication system 216 causes each one of theselected plurality of smart replies to be displayed on the secondcomputing device of the second user as a corresponding selectable userinterface element. For example, the communication system 216 maytransmit the selected plurality of smart replies to the second computingdevice along with instructions to display the selected plurality ofsmart replies, thereby causing the second computing device to displaythe selected plurality of smart replies as corresponding selectable userinterface elements.

At operation 1050, the communication system 216 receives a userselection of one of the plurality of smart replies from the secondcomputing device. For example, the second user may click or tap on oneof the smart replies, and the communication system 216 may receive anindication of the click or tap as the user selection.

At operation 1060, the communication system 216 transmits a secondmessage including the selected one of the plurality of smart replies tothe first computing device in response to, or otherwise based on, thereceiving of the user selection. For example, the receiving of the userselection may trigger the automatic creation of the second messageincluding the selected smart reply and the automatic transmission of thesecond message to the first computing device.

At operation 1052, the communication system 216, in response to orotherwise based on the receiving of the user selection at operation1050, stores a record of the user selection of the one of the pluralityof smart replies in a database. At operation 1054, the communicationsystem 216 modifies one or more models used to generate the smartreplies based on the record of the user selection of the one of theplurality of smart replies using one or more machine learningoperations. In some example embodiments, the record of the userselection of the one of the plurality of smart replies is used astraining data in the one or more machine learning operations.

It is contemplated that any of the other features described within thepresent disclosure can be incorporated into the method 1000.

FIG. 11 is a flowchart illustrating a method of generating smartfollow-up contents, in accordance with an example embodiment. The method1100 can be performed by processing logic that can comprise hardware(e.g., circuitry, dedicated logic, programmable logic, microcode, etc.),software (e.g., instructions run on a processing device), or acombination thereof. In one implementation, the method 1100 is performedby the communication system 216 of FIGS. 2-3, as described above.

In some example embodiments, operation 1110 performed in response to orotherwise based on operation 1060 of FIG. 10. At operation 1110, thecommunication system 216 detects that a message that includes a selectedsmart reply has been transmitted from the second computing device of thesecond user to the first computing device of the first user.

At operation 1120, the communication system 216 selects a plurality ofsmart follow-up content from a plurality of candidate follow-up contentin response to the transmitting of the second message including theselected one of the plurality of smart replies to the first computingdevice. In some example embodiments, the selecting of the plurality ofsmart follow-up content is based on at least one of the selected one ofthe plurality of smart replies and the first content of the first set ofone or more messages. In some example embodiments, the selecting of theplurality of smart follow-up content comprises determining the pluralityof candidate follow-up content based on at least one of the selected oneof the plurality of smart replies and the first content of the first setof one or more messages, and selecting the plurality of smart follow-upcontent from the plurality of candidate follow-up content using thehierarchical graph data structure and at least one diversity rule, whereeach one of the plurality of smart follow-up content is represented by acorresponding one of the plurality of leaf nodes in the hierarchicalgraph data structure. In some example embodiments, the selecting of theplurality of smart follow-up content comprises omitting at least one ofthe plurality of candidate follow-up content from selection to beincluded in the plurality of smart follow-up content based on thediversity rule(s), where the diversity rule(s) limit a number of theplurality of smart follow-up content that have a common parent node orhave some other common relationship with a particular node.

At operation 1130, the communication system 216 causes each one of theplurality of smart follow-up content to be displayed on the secondcomputing device of the second user as a corresponding selectable userinterface element. For example, the communication system 216 maytransmit the plurality of smart follow-up content to the secondcomputing device along with instructions to display the plurality ofsmart follow-up content, thereby causing the second computing device todisplay the plurality of smart follow-up content as correspondingselectable user interface elements.

At operation 1140, the communication system 216 receives a userselection of one of the plurality of smart follow-up content from thesecond computing device. For example, the second user may click or tapon one of the smart follow-up content, and the communication system 216may receive an indication of the click or tap as the user selection.

At operation 1150, the communication system 216 transmits anothermessage including the selected one of the plurality of smart follow-upcontent to the first computing device in response to, or otherwise basedon, the receiving of the user selection. For example, the receiving ofthe user selection may trigger the automatic creation of the othermessage including the selected smart follow-up content and the automatictransmission of the other message to the first computing device.

At operation 1142, the communication system 216, in response to orotherwise based on the receiving of the user selection at operation1140, stores a record of the user selection of the one of the pluralityof smart replies in a database. At operation 1144, the communicationsystem 216 modifies one or more models used to generate the plurality ofsmart follow-up content based on the record of the user selection of theone of the plurality of smart follow-up content using one or moremachine learning operations. In some example embodiments, the record ofthe user selection of the one of the plurality of smart follow-upcontent is used as training data in the one or more machine learningoperations.

It is contemplated that any of the other features described within thepresent disclosure can be incorporated into the method 1000.

Example Mobile Device

FIG. 12 is a block diagram illustrating a mobile device 1200, accordingto an example embodiment. The mobile device 1200 can include a processor1202. The processor 1202 can be any of a variety of different types ofcommercially available processors suitable for mobile devices 1200 (forexample, an XScale architecture microprocessor, a Microprocessor withoutInterlocked Pipeline Stages (MIPS) architecture processor, or anothertype of processor). A memory 1204, such as a random access memory (RAM),a Flash memory, or other type of memory, is typically accessible to theprocessor 1202. The memory 1204 can be adapted to store an operatingsystem (OS) 1206, as well as application programs 1208, such as a mobilelocation-enabled application that can provide location-based services(LBSs) to a user. The processor 1202 can be coupled, either directly orvia appropriate intermediary hardware, to a display 1210 and to one ormore input/output (I/O) devices 1212, such as a keypad, a touch panelsensor, a microphone, and the like. Similarly, in some embodiments, theprocessor 1202 can be coupled to a transceiver 1214 that interfaces withan antenna 1216. The transceiver 1214 can be configured to both transmitand receive cellular network signals, wireless data signals, or othertypes of signals via the antenna 1216, depending on the nature of themobile device 1200. Further, in some configurations, a GPS receiver 1218can also make use of the antenna 1216 to receive GPS signals.

Modules, Components and Logic

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may constitute eithersoftware modules (e.g., code embodied (1) on a non-transitorymachine-readable medium or (2) in a transmission signal) orhardware-implemented modules. A hardware-implemented module is tangibleunit capable of performing certain operations and may be configured orarranged in a certain manner. In example embodiments, one or morecomputer systems (e.g., a standalone, client or server computer system)or one or more processors may be configured by software (e.g., anapplication or application portion) as a hardware-implemented modulethat operates to perform certain operations as described herein.

In various embodiments, a hardware-implemented module may be implementedmechanically or electronically. For example, a hardware-implementedmodule may comprise dedicated circuitry or logic that is permanentlyconfigured (e.g., as a special-purpose processor, such as a fieldprogrammable gate array (FPGA) or an application-specific integratedcircuit (ASIC)) to perform certain operations. A hardware-implementedmodule may also comprise programmable logic or circuitry (e.g., asencompassed within a general-purpose processor or other programmableprocessor) that is temporarily configured by software to perform certainoperations. It will be appreciated that the decision to implement ahardware-implemented module mechanically, in dedicated and permanentlyconfigured circuitry, or in temporarily configured circuitry (e.g.,configured by software) may be driven by cost and time considerations.

Accordingly, the term “hardware-implemented module” should be understoodto encompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired) or temporarily ortransitorily configured (e.g., programmed) to operate in a certainmanner and/or to perform certain operations described herein.Considering embodiments in which hardware-implemented modules aretemporarily configured (e.g., programmed), each of thehardware-implemented modules need not be configured or instantiated atany one instance in time. For example, where the hardware-implementedmodules comprise a general-purpose processor configured using software,the general-purpose processor may be configured as respective differenthardware-implemented modules at different times. Software mayaccordingly configure a processor, for example, to constitute aparticular hardware-implemented module at one instance of time and toconstitute a different hardware-implemented module at a differentinstance of time.

Hardware-implemented modules can provide information to, and receiveinformation from, other hardware-implemented modules. Accordingly, thedescribed hardware-implemented modules may be regarded as beingcommunicatively coupled. Where multiple of such hardware-implementedmodules exist contemporaneously, communications may be achieved throughsignal transmission (e.g., over appropriate circuits and buses) thatconnect the hardware-implemented modules. In embodiments in whichmultiple hardware-implemented modules are configured or instantiated atdifferent times, communications between such hardware-implementedmodules may be achieved, for example, through the storage and retrievalof information in memory structures to which the multiplehardware-implemented modules have access. For example, onehardware-implemented module may perform an operation, and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware-implemented module may then,at a later time, access the memory device to retrieve and process thestored output. Hardware-implemented modules may also initiatecommunications with input or output devices, and can operate on aresource (e.g., a collection of information).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods described herein may be at least partiallyprocessor-implemented. For example, at least some of the operations of amethod may be performed by one or more processors orprocessor-implemented modules. The performance of certain of theoperations may be distributed among the one or more processors, not onlyresiding within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor or processors maybe located in a single location (e.g., within a home environment, anoffice environment or as a server farm), while in other embodiments theprocessors may be distributed across a number of locations.

The one or more processors may also operate to support performance ofthe relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations may be performed by a group of computers (as examples ofmachines including processors), these operations being accessible via anetwork (e.g., the Internet) and via one or more appropriate interfaces(e.g., Application Program Interfaces (APIs).)

Electronic Apparatus and System

Example embodiments may be implemented in digital electronic circuitry,or in computer hardware, firmware, software, or in combinations of them.Example embodiments may be implemented using a computer program product,e.g., a computer program tangibly embodied in an information carrier,e.g., in a machine-readable medium for execution by, or to control theoperation of, data processing apparatus, e.g., a programmable processor,a computer, or multiple computers.

A computer program can be written in any form of programming language,including compiled or interpreted languages, and it can be deployed inany form, including as a stand-alone program or as a module, subroutine,or other unit suitable for use in a computing environment. A computerprogram can be deployed to be executed on one computer or on multiplecomputers at one site or distributed across multiple sites andinterconnected by a communication network.

In example embodiments, operations may be performed by one or moreprogrammable processors executing a computer program to performfunctions by operating on input data and generating output. Methodoperations can also be performed by, and apparatus of exampleembodiments may be implemented as, special purpose logic circuitry,e.g., a field programmable gate array (FPGA) or an application-specificintegrated circuit (ASIC).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. Inembodiments deploying a programmable computing system, it will beappreciated that both hardware and software architectures meritconsideration. Specifically, it will be appreciated that the choice ofwhether to implement certain functionality in permanently configuredhardware (e.g., an ASIC), in temporarily configured hardware (e.g., acombination of software and a programmable processor), or a combinationof permanently and temporarily configured hardware may be a designchoice. Below are set out hardware (e.g., machine) and softwarearchitectures that may be deployed, in various example embodiments.

Example Machine Architecture and Machine-Readable Medium

FIG. 13 is a block diagram of an example computer system 1300 on whichmethodologies described herein may be executed, in accordance with anexample embodiment. In alternative embodiments, the machine operates asa standalone device or may be connected (e.g., networked) to othermachines. In a networked deployment, the machine may operate in thecapacity of a server or a client machine in server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine may be a personal computer (PC), atablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), acellular telephone, a web appliance, a network router, switch or bridge,or any machine capable of executing instructions (sequential orotherwise) that specify actions to be taken by that machine. Further,while only a single machine is illustrated, the term “machine” shallalso be taken to include any collection of machines that individually orjointly execute a set (or multiple sets) of instructions to perform anyone or more of the methodologies discussed herein.

The example computer system 1300 includes a processor 1302 (e.g., acentral processing unit (CPU), a graphics processing unit (GPU) orboth), a main memory 1304 and a static memory 1306, which communicatewith each other via a bus 1308. The computer system 1300 may furtherinclude a graphics display unit 1310 (e.g., a liquid crystal display(LCD) or a cathode ray tube (CRT)). The computer system 1300 alsoincludes an alphanumeric input device 1312 (e.g., a keyboard or atouch-sensitive display screen), a user interface (UI) navigation device1314 (e.g., a mouse), a storage unit 1316, a signal generation device1318 (e.g., a speaker) and a network interface device 1320.

Machine-Readable Medium

The storage unit 1316 includes a machine-readable medium 1322 on whichis stored one or more sets of instructions and data structures (e.g.,software) 1324 embodying or utilized by any one or more of themethodologies or functions described herein. The instructions 1324 mayalso reside, completely or at least partially, within the main memory1304 and/or within the processor 1302 during execution thereof by thecomputer system 1300, the main memory 1304 and the processor 1302 alsoconstituting machine-readable media.

While the machine-readable medium 1322 is shown in an example embodimentto be a single medium, the term “machine-readable medium” may include asingle medium or multiple media (e.g., a centralized or distributeddatabase, and/or associated caches and servers) that store the one ormore instructions 1324 or data structures. The term “machine-readablemedium” shall also be taken to include any tangible medium that iscapable of storing, encoding or carrying instructions (e.g.,instructions 1324) for execution by the machine and that cause themachine to perform any one or more of the methodologies of the presentdisclosure, or that is capable of storing, encoding or carrying datastructures utilized by or associated with such instructions. The term“machine-readable medium” shall accordingly be taken to include, but notbe limited to, solid-state memories, and optical and magnetic media.Specific examples of machine-readable media include non-volatile memory,including by way of example semiconductor memory devices, e.g., ErasableProgrammable Read-Only Memory (EPROM), Electrically ErasableProgrammable Read-Only Memory (EEPROM), and flash memory devices;magnetic disks such as internal hard disks and removable disks;magneto-optical disks; and CD-ROM and DVD-ROM disks.

Transmission Medium

The instructions 1324 may further be transmitted or received over acommunications network 1326 using a transmission medium. Theinstructions 1324 may be transmitted using the network interface device1320 and any one of a number of well-known transfer protocols (e.g.,HTTP). Examples of communication networks include a local area network(“LAN”), a wide area network (“WAN”), the Internet, mobile telephonenetworks, Plain Old Telephone Service (POTS) networks, and wireless datanetworks (e.g., WiFi and WiMax networks). The term “transmission medium”shall be taken to include any intangible medium that is capable ofstoring, encoding or carrying instructions for execution by the machine,and includes digital or analog communications signals or otherintangible media to facilitate communication of such software.

Although an embodiment has been described with reference to specificexample embodiments, it will be evident that various modifications andchanges may be made to these embodiments without departing from thebroader spirit and scope of the present disclosure. Accordingly, thespecification and drawings are to be regarded in an illustrative ratherthan a restrictive sense. The accompanying drawings that form a parthereof, show by way of illustration, and not of limitation, specificembodiments in which the subject matter may be practiced. Theembodiments illustrated are described in sufficient detail to enablethose skilled in the art to practice the teachings disclosed herein.Other embodiments may be utilized and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. This Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.Although specific embodiments have been illustrated and describedherein, it should be appreciated that any arrangement calculated toachieve the same purpose may be substituted for the specific embodimentsshown. This disclosure is intended to cover any and all adaptations orvariations of various embodiments. Combinations of the aboveembodiments, and other embodiments not specifically described herein,will be apparent to those of skill in the art upon reviewing the abovedescription.

1. (canceled)
 2. A computer-implemented method comprising: detecting, bya computer system comprising a memory and at least one hardwareprocessor, that a first set of one or more messages having first contenthas been transmitted from a first computing device of a first user to asecond computing device of a second user; selecting, by the computersystem, a plurality of category concepts based on the first content ofthe first set of one or more messages using a hierarchical graph datastructure, the hierarchical graph data structure comprising a tree ofconcepts ranging from a root node to a plurality of leaf nodes with atleast one intermediate node in between the root node and each one of theplurality of leaf nodes, each one of the plurality of category conceptsbeing represented by a corresponding intermediate node in thehierarchical graph data structure; causing, by the computer system, eachone of the plurality of category concepts to be displayed on the secondcomputing device of the second user as a corresponding selectable userinterface element; receiving, by the computer system, a first userselection of the corresponding selectable user interface element of oneof the plurality of category concepts from the second computing device;and causing, by the computer system, a plurality of smart replies to bedisplayed on the second computing device in response to the userselection of the corresponding selectable user interface element of theone of the plurality of category concepts, the plurality of smartreplies corresponding to leaf nodes of the intermediate node thatcorresponds to the one of the plurality of category concepts in thehierarchical graph data structure.
 3. The computer-implemented method ofclaim 2, further comprising: receiving, by the computer system, a seconduser selection of one of the plurality of smart replies from the secondcomputing device from the second computing device; and transmitting, bythe computer system, a second message including the selected one of theplurality of smart replies to the first computing device in response tothe receiving of the second user selection.
 4. The computer-implementedmethod of claim 2, wherein the causing the plurality of smart replies tobe displayed on the second computing device comprises: determining aplurality of candidate replies based on the first content of the firstset of one or more messages; and selecting the plurality of smartreplies from the plurality of candidate replies, the selecting theplurality of smart replies comprising omitting at least one of theplurality of candidate replies from selection to be included in theplurality of smart replies based on at least one diversity rule, the atleast one diversity rule limiting a number of the plurality of smartreplies that have a common parent node, wherein the causing theplurality of smart replies to be displayed on the second computingdevice is based on the selecting of the plurality of smart replies fromthe plurality of candidate replies.
 5. The computer-implemented methodof claim 2, wherein the first content of the first set of one or moremessages comprises text.
 6. The computer-implemented method of claim 2,wherein each one of the plurality of smart replies comprises text. 7.The computer-implemented method of claim 2, wherein the computer systemcomprises a remote server.
 8. The computer-implemented method of claim2, wherein the computer system comprises the second computing device. 9.A computer system comprising: at least one hardware processor; and anon-transitory machine-readable medium embodying a set of instructionsthat, when executed by the at least one hardware processor, cause the atleast one processor to perform operations, the operations comprising:detecting that a first set of one or more messages having first contenthas been transmitted from a first computing device of a first user to asecond computing device of a second user; selecting a plurality ofcategory concepts based on the first content of the first set of one ormore messages using a hierarchical graph data structure, thehierarchical graph data structure comprising a tree of concepts rangingfrom a root node to a plurality of leaf nodes with at least oneintermediate node in between the root node and each one of the pluralityof leaf nodes, each one of the plurality of category concepts beingrepresented by a corresponding intermediate node in the hierarchicalgraph data structure; causing each one of the plurality of categoryconcepts to be displayed on the second computing device of the seconduser as a corresponding selectable user interface element; receiving afirst user selection of the corresponding selectable user interfaceelement of one of the plurality of category concepts from the secondcomputing device; and causing a plurality of smart replies to bedisplayed on the second computing device in response to the userselection of the corresponding selectable user interface element of theone of the plurality of category concepts, the plurality of smartreplies corresponding to leaf nodes of the intermediate node thatcorresponds to the one of the plurality of category concepts in thehierarchical graph data structure.
 10. The system of claim 9, whereinthe operations further comprise: receiving a second user selection ofone of the plurality of smart replies from the second computing devicefrom the second computing device; and transmitting a second messageincluding the selected one of the plurality of smart replies to thefirst computing device in response to the receiving of the second userselection.
 11. The system of claim 9, wherein the causing the pluralityof smart replies to be displayed on the second computing devicecomprises: determining a plurality of candidate replies based on thefirst content of the first set of one or more messages; and selectingthe plurality of smart replies from the plurality of candidate replies,the selecting the plurality of smart replies comprising omitting atleast one of the plurality of candidate replies from selection to beincluded in the plurality of smart replies based on at least onediversity rule, the at least one diversity rule limiting a number of theplurality of smart replies that have a common parent node, wherein thecausing the plurality of smart replies to be displayed on the secondcomputing device is based on the selecting of the plurality of smartreplies from the plurality of candidate replies.
 12. The system of claim9, wherein the first content of the first set of one or more messagescomprises text.
 13. The system of claim 9, wherein each one of theplurality of smart replies comprises text.
 14. The system of claim 9,wherein the computer system comprises a remote server.
 15. The system ofclaim 9, wherein the computer system comprises the second computingdevice.
 16. A non-transitory machine-readable medium embodying a set ofinstructions that, when executed by at least one hardware processor,cause the processor to perform operations, the operations comprising:detecting, by a computer system comprising the at least one hardwareprocessor, that a first set of one or more messages having first contenthas been transmitted from a first computing device of a first user to asecond computing device of a second user; selecting, by the computersystem, a plurality of category concepts based on the first content ofthe first set of one or more messages using a hierarchical graph datastructure, the hierarchical graph data structure comprising a tree ofconcepts ranging from a root node to a plurality of leaf nodes with atleast one intermediate node in between the root node and each one of theplurality of leaf nodes, each one of the plurality of category conceptsbeing represented by a corresponding intermediate node in thehierarchical graph data structure; causing, by the computer system, eachone of the plurality of category concepts to be displayed on the secondcomputing device of the second user as a corresponding selectable userinterface element; receiving, by the computer system, a first userselection of the corresponding selectable user interface element of oneof the plurality of category concepts from the second computing device;and causing, by the computer system, a plurality of smart replies to bedisplayed on the second computing device in response to the userselection of the corresponding selectable user interface element of theone of the plurality of category concepts, the plurality of smartreplies corresponding to leaf nodes of the intermediate node thatcorresponds to the one of the plurality of category concepts in thehierarchical graph data structure.
 17. The non-transitorymachine-readable medium of claim 16, wherein the operations furthercomprise: receiving, by the computer system, a second user selection ofone of the plurality of smart replies from the second computing devicefrom the second computing device; and transmitting, by the computersystem, a second message including the selected one of the plurality ofsmart replies to the first computing device in response to the receivingof the second user selection.
 18. The non-transitory machine-readablemedium of claim 16, wherein the causing the plurality of smart repliesto be displayed on the second computing device comprises: determining aplurality of candidate replies based on the first content of the firstset of one or more messages; and selecting the plurality of smartreplies from the plurality of candidate replies, the selecting theplurality of smart replies comprising omitting at least one of theplurality of candidate replies from selection to be included in theplurality of smart replies based on at least one diversity rule, the atleast one diversity rule limiting a number of the plurality of smartreplies that have a common parent node, wherein the causing theplurality of smart replies to be displayed on the second computingdevice is based on the selecting of the plurality of smart replies fromthe plurality of candidate replies.
 19. The non-transitorymachine-readable medium of claim 16, wherein the first content of thefirst set of one or more messages comprises text.
 20. The non-transitorymachine-readable medium of claim 16, wherein each one of the pluralityof smart replies comprises text.
 21. The non-transitory machine-readablemedium of claim 16, wherein the computer system comprises a remoteserver.